UCL Discovery
UCL home » Library Services » Electronic resources » UCL Discovery

Causal Discovery Under a Confounder Blanket

Watson, DS; Silva, R; (2022) Causal Discovery Under a Confounder Blanket. In: Proceedings of the 38th Conference on Uncertainty in Artificial Intelligence (UAI 2022). (pp. pp. 2096-2106). PMLR 180 Green open access

[thumbnail of watson22a.pdf]
Preview
Text
watson22a.pdf - Published Version

Download (574kB) | Preview

Abstract

Inferring causal relationships from observational data is rarely straightforward, but the problem is especially difficult in high dimensions. For these applications, causal discovery algorithms typically require parametric restrictions or extreme sparsity constraints. We relax these assumptions and focus on an important but more specialized problem, namely recovering the causal order among a subgraph of variables known to descend from some (possibly large) set of confounding covariates, i.e. a confounder blanket. This is useful in many settings, for example when studying a dynamic biomolecular subsystem with genetic data providing background information. Under a structural assumption called the confounder blanket principle, which we argue is essential for tractable causal discovery in high dimensions, our method accommodates graphs of low or high sparsity while maintaining polynomial time complexity. We present a structure learning algorithm that is provably sound and complete with respect to a so-called lazy oracle. We design inference procedures with finite sample error control for linear and nonlinear systems, and demonstrate our approach on a range of simulated and real-world datasets. An accompanying R package, cbl, is available from CRAN.

Type: Proceedings paper
Title: Causal Discovery Under a Confounder Blanket
Event: 38th Conference on Uncertainty in Artificial Intelligence (UAI 2022)
Open access status: An open access version is available from UCL Discovery
Publisher version: https://proceedings.mlr.press/v180/watson22a/watso...
Language: English
Additional information: This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Keywords: stat.ME, stat.ME, cs.AI, stat.ML
UCL classification: UCL
UCL > Provost and Vice Provost Offices > UCL BEAMS
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Maths and Physical Sciences
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Maths and Physical Sciences > Dept of Statistical Science
URI: https://discovery.ucl.ac.uk/id/eprint/10167115
Downloads since deposit
6Downloads
Download activity - last month
Download activity - last 12 months
Downloads by country - last 12 months

Archive Staff Only

View Item View Item